Underwater Acoustic Image Denoising Using Stationary Wavelet Transform and Various Shrinkage Functions
Underwater acoustic images are captured by sonar technology which uses sound as a source. The noise in the acoustic images may occur only during acquisition. These noises may be multiplicative in nature and cause serious effects on the images affecting their visual quality. Generally image denoising...
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| Format: | Article |
| Language: | English |
| Published: |
Computer Vision Center Press
2021-09-01
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| Series: | ELCVIA Electronic Letters on Computer Vision and Image Analysis |
| Subjects: | |
| Online Access: | https://elcvia.cvc.uab.es/article/view/1360 |
| _version_ | 1818901946854014976 |
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| author | Priyadharsini Ravisankar |
| author_facet | Priyadharsini Ravisankar |
| author_sort | Priyadharsini Ravisankar |
| collection | DOAJ |
| description | Underwater acoustic images are captured by sonar technology which uses sound as a source. The noise in the acoustic images may occur only during acquisition. These noises may be multiplicative in nature and cause serious effects on the images affecting their visual quality. Generally image denoising techniques that remove the noise from the images can use linear and non-linear filters. In this paper, wavelet based denoising method is used to reduce the noise from the images. The image is decomposed using Stationary Wavelet Transform (SWT) into low and high frequency components. The various shrinkage functions such as Visushrink and Sureshrink are used for selecting the threshold to remove the undesirable signals in the low frequency component. The high frequency components such as edges and corners are retained. Then the inverse SWT is used for reconstruction of denoised image by combining the modified low frequency components with the high frequency components. The performance measure Peak Signal to Noise Ratio (PSNR) is obtained for various wavelets such as Haar, Daubechies,Coiflet and by changing the thresholding methods. |
| first_indexed | 2024-12-19T20:27:50Z |
| format | Article |
| id | doaj.art-7030a149ba0848f0bda7988c079f0ae6 |
| institution | Directory Open Access Journal |
| issn | 1577-5097 |
| language | English |
| last_indexed | 2024-12-19T20:27:50Z |
| publishDate | 2021-09-01 |
| publisher | Computer Vision Center Press |
| record_format | Article |
| series | ELCVIA Electronic Letters on Computer Vision and Image Analysis |
| spelling | doaj.art-7030a149ba0848f0bda7988c079f0ae62022-12-21T20:06:47ZengComputer Vision Center PressELCVIA Electronic Letters on Computer Vision and Image Analysis1577-50972021-09-0120210.5565/rev/elcvia.1360Underwater Acoustic Image Denoising Using Stationary Wavelet Transform and Various Shrinkage FunctionsPriyadharsini Ravisankar0Sri Sivasubramanya Nadar College of EngineeringUnderwater acoustic images are captured by sonar technology which uses sound as a source. The noise in the acoustic images may occur only during acquisition. These noises may be multiplicative in nature and cause serious effects on the images affecting their visual quality. Generally image denoising techniques that remove the noise from the images can use linear and non-linear filters. In this paper, wavelet based denoising method is used to reduce the noise from the images. The image is decomposed using Stationary Wavelet Transform (SWT) into low and high frequency components. The various shrinkage functions such as Visushrink and Sureshrink are used for selecting the threshold to remove the undesirable signals in the low frequency component. The high frequency components such as edges and corners are retained. Then the inverse SWT is used for reconstruction of denoised image by combining the modified low frequency components with the high frequency components. The performance measure Peak Signal to Noise Ratio (PSNR) is obtained for various wavelets such as Haar, Daubechies,Coiflet and by changing the thresholding methods.https://elcvia.cvc.uab.es/article/view/1360Acoustic imagesCoifletDaubachiesHaarStationary WaveletSureshrink |
| spellingShingle | Priyadharsini Ravisankar Underwater Acoustic Image Denoising Using Stationary Wavelet Transform and Various Shrinkage Functions ELCVIA Electronic Letters on Computer Vision and Image Analysis Acoustic images Coiflet Daubachies Haar Stationary Wavelet Sureshrink |
| title | Underwater Acoustic Image Denoising Using Stationary Wavelet Transform and Various Shrinkage Functions |
| title_full | Underwater Acoustic Image Denoising Using Stationary Wavelet Transform and Various Shrinkage Functions |
| title_fullStr | Underwater Acoustic Image Denoising Using Stationary Wavelet Transform and Various Shrinkage Functions |
| title_full_unstemmed | Underwater Acoustic Image Denoising Using Stationary Wavelet Transform and Various Shrinkage Functions |
| title_short | Underwater Acoustic Image Denoising Using Stationary Wavelet Transform and Various Shrinkage Functions |
| title_sort | underwater acoustic image denoising using stationary wavelet transform and various shrinkage functions |
| topic | Acoustic images Coiflet Daubachies Haar Stationary Wavelet Sureshrink |
| url | https://elcvia.cvc.uab.es/article/view/1360 |
| work_keys_str_mv | AT priyadharsiniravisankar underwateracousticimagedenoisingusingstationarywavelettransformandvariousshrinkagefunctions |